A Subpath Kernel for Learning Hierarchical Image Representations

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9069)

Abstract

Tree kernels have demonstrated their ability to deal with hierarchical data, as the intrinsic tree structure often plays a discriminative role. While such kernels have been successfully applied to various domains such as nature language processing and bioinformatics, they mostly concentrate on ordered trees and whose nodes are described by symbolic data. Meanwhile, hierarchical representations have gained increasing interest to describe image content. This is particularly true in remote sensing, where such representations allow for revealing different objects of interest at various scales through a tree structure. However, the induced trees are unordered and the nodes are equipped with numerical features. In this paper, we propose a new structured kernel for hierarchical image representations which is built on the concept of subpath kernel. Experimental results on both artificial and remote sensing datasets show that the proposed kernel manages to deal with the hierarchical nature of the data, leading to better classification rates.

Keywords

Hierarchical representation Image classification Structured kernels Subpath kernel 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Yanwei Cui
    • 1
  • Laetitia Chapel
    • 1
  • Sébastien Lefèvre
    • 1
  1. 1.IRISA (UMR 6074)University Bretagne-SudVannesFrance

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